TITLE: Recognizing Syntactic Errors in the Writing of Second
Language Learners
AUTHORS: David A. Schneider and Kathleen F. McCoy
COMMENTS: In Proceedings of the Thirty-Sixth Annual Meeting of the
Association for Computational Linguistics and the Seventeenth
International Conference on Computational Linguistics
This paper reports on the recognition component of an intelligent
tutoring system that is designed to help foreign language speakers
learn standard English. The system models the grammar of the learner,
with this instantiation of the system tailored to signers of American
Sign Language (ASL). We discuss the theoretical motivations for the
system, various difficulties that have been encountered in the
implementation, as well as the methods we have used to overcome these
problems. Our method of capturing ungrammaticalities involves using
mal-rules (also called 'error productions'). However, the
straightforward addition of some mal-rules causes significant
performance problems with the parser. For instance, the ASL population
has a strong tendency to drop pronouns and the auxiliary verb `to
be'. Being able to account for these as sentences results in an
explosion in the number of possible parses for each sentence. This
explosion, left unchecked, greatly hampers the performance of the
system. We discuss how this is handled by taking into account
expectations from the specific population (some of which are captured
in our unique user model). The different representations of lexical
items at various points in the acquisition process are modeled by
using mal-rules, which obviates the need for multiple lexicons. The
grammar is evaluated on its ability to correctly diagnose agreement
problems in actual sentences produced by ASL native speakers.